Jupyter

You should build a data analytics project in a Jupyter notebook. In particular, you should do the following:

• Load the Gapminder dataset using the below code cell.
• Extract and visualize interesting insights about countries using Pandas and Plotly.
• Structure, document, and decorate your notebook using Markdown.

import pandas as pd
import plotly
import plotly.express as px
import plotly.graph_objects as go

df = px.data.gapminder()
df.head()

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4

canada = df[df["country"]=="Canada"]
canada.head()

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
240	Canada	Americas	1952	68.75	14785584	11367.16112	CAN	124
241	Canada	Americas	1957	69.96	17010154	12489.95006	CAN	124
242	Canada	Americas	1962	71.30	18985849	13462.48555	CAN	124
243	Canada	Americas	1967	72.13	20819767	16076.58803	CAN	124
244	Canada	Americas	1972	72.88	22284500	18970.57086	CAN	124

import plotly.express as px

df = px.data.gapminder().query("country=='Canada'")
fig = px.line(df, x="year", y="pop", title='Population in Canada')
fig.show()

import plotly.express as px

df = px.data.gapminder().query("continent=='Europe'")
fig = px.line(df, x="year", y="pop", color='country')
fig.show()

import plotly.express as px
df = px.data.gapminder()

fig = px.scatter(df.query("year==2007"), x="gdpPercap", y="lifeExp",
	         size="pop", color="continent",
                 hover_name="country", log_x=True, size_max=60)
fig.show()

import plotly.express as px
df = px.data.gapminder().query("year == 2007").query("continent == 'Europe'")
df.loc[df['pop'] < 2.e6, 'country'] = 'Other countries' # Represent only large countries
fig = px.pie(df, values='pop', names='country', title='Population of European continent')
fig.show()

import plotly.express as px
# This dataframe has 244 lines, but 4 distinct values for `day`
df = px.data.gapminder().query("continent == 'Asia'").query("year==2007")
fig = px.pie(df, values='pop', names='country', title='Population of Asia in 2007')
fig.show()

import plotly.express as px
df = px.data.gapminder().query("continent == 'Asia'")
fig = px.area(df, x="year", y="pop")
fig.show()

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